What Confluent can tell us about video sensing applications?
Confluent’s Streaming Data platform is a cloud-native, fully managed event streaming system built on Apache Kafka but rearchitected from the ground up for elastic scalability, real-time processing, and enterprise-grade governance. At its heart, the platform turns raw data in motion into reliable, governed data products that power real-time applications, analytics, and AI.
The Foundation: KORA, Confluent’s Cloud-Native Kafka Engine
Everything starts with KORA, Confluent’s custom-engineered version of Apache Kafka. Unlike traditional Kafka deployments, KORA is designed for a multi-tenant, serverless cloud architecture. It delivers millions of messages per second with sub-10ms latency and guarantees 99.99% uptime through multi-availability-zone clustering. Topics are partitioned across brokers for horizontal scale and fault tolerance, and producers and consumers are fully decoupled—meaning you can add or evolve services without breaking dependencies.
Storage That Scales: Tiered Storage Architecture
One of KORA’s most powerful innovations is its three-tier storage system, which replaces Kafka’s traditional single-layer local-disk storage:
• Hot tier (memory/SSD): Stores recent data for ultra-low-latency access.
• Warm tier (local SSD cache): Handles intermediate retention.
• Cold tier (cloud object storage like S3, GCS, or Azure Blob): Provides infinite, cost-effective retention.
After data segments are flushed, they’re automatically moved to colder, cheaper storage while metadata is tracked internally. This separation of compute and storage lets you scale each independently and retain data for months or years at a fraction of the cost—something vanilla Kafka can’t do efficiently.
Governance and Quality: Schema Registry and Stream Governance
To keep streaming data trustworthy, Confluent includes a centralized Schema Registry that manages Avro, Protobuf, and JSON Schema with strict compatibility rules (backward, forward, full, or none). This ensures producers and consumers stay in sync even as schemas evolve.
Built on top is the Stream Governance suite, which delivers three critical capabilities:
1. Stream Quality: Enforces data contracts with schema validation and business rule checks.
2. Stream Catalog: Provides data discovery with tagging and rich business metadata.
3. Stream Lineage: Maps end-to-end event flows, showing exactly where data comes from and where it goes.
Together, these tools turn chaotic data streams into governed, high-quality data products.
Connectors: Plug-and-Play Data Integration
Confluent ships with 120+ pre-built Kafka connectors for databases, data warehouses, cloud services, and more. These source and sink connectors abstract away the complexity of data integration. You can also apply transformations on the fly using Single Message Transformations (SMTs), making it easy to clean, enrich, or reformat data as it moves through the platform.
Stream Processing: Real-Time Computation at Scale
For real-time computation, the platform supports multiple processing engines:
• Apache Flink®: A powerful engine for stateful stream processing with automatic schema evolution handling.
• Kafka Streams: A lightweight client library for building stream processing applications with a processor topology of source, processor, and sink nodes. It uses a depth-first processing strategy and partition-based state stores, avoiding backpressure issues.
• ksqlDB: A streaming SQL engine that lets you query and transform data using familiar SQL syntax.
• Tableflow: Creates materialized views for real-time analytics.
The platform even supports LLM and ML model inference directly inside stream processing, enabling streaming agents that can invoke external tools—bringing AI capabilities into real-time data pipelines.
Multi-Cloud, Hybrid, and Geo-Replication
Confluent is built for modern cloud realities:
• Cluster Linking enables geo-replication across clusters and clouds.
• Multi-cloud support includes native integration with S3, GCS, and Azure Blob, plus a Bring-Your-Own-Cloud (BYOC) option.
• Networking security includes private linking, VPC peering, and end-to-end in-transit encryption.
• The architecture supports Kappa architecture, unifying operational and analytical workloads in a single pipeline.
This flexibility lets you run Confluent consistently across AWS, GCP, Azure, or on-premises environments.
How Data Flows Through the Platform
Imagine this journey:
1. Producers send events into Kafka topics, which are partitioned distributed logs.
2. Schema Registry validates each event against its schema.
3. Data lands in tiered storage, automatically moving from hot to cold as it ages.
4. Connectors pull data in from or push data out to external systems.
5. Flink, Kafka Streams, or ksqlDB process the data in real time.
6. Processed data flows to consumer applications, data warehouses, analytics dashboards, or AI models.
Because producers and consumers are decoupled, you can add, remove, or scale any part of this pipeline without disrupting the rest.
Why Confluent Stands Out
Compared to vanilla Kafka, Confluent delivers:
• Tiered storage for infinite retention at low cost.
• Auto-scaling that’s 30× faster than manual Kafka rebalancing.
• Built-in governance with Schema Registry and Stream Governance.
• Fully managed operations with a 99.99% uptime SLA.
• Multiple processing engines: Flink + ksqlDB + Kafka Streams, not just one.
In short, Confluent’s Streaming Data platform transforms the challenge of managing real-time data into a seamless, governed, and scalable experience—enabling event-driven architectures, real-time analytics, and AI applications powered by high-quality, trusted data in motion.
What this architecture informs for AI Agents specifically for drone video sensing applications
AI agents are usually arranged in one of the following patterns:
• Automatic Query Decomposition by one agent co-ordinating with other agents to invoke each of the queries incurring token costs in parallel per agent.
• Lambda processing or function app agents: scaling to workload for predefined routines on a task by task basis.
• Reasoning agent: forming a breakdown of step-by-step tasks for execution and query response reconstitution.
• Model Context Protocol enabled Agents: for agents to independently reach each other for fulfilment.
• Grounding Agents: with connectivity to online or specific data sources or services.
What Confluent architecture suggests is to perform this at an event-by-event basis on a perpetual agent as follows:
package com.sms.event;
/**
* This represents an observable notification.
* @param <T> The type of event that is to be observed.
*/
public interface Notifier<T> {
/**
* Attach a listener for notification type T.
* @param listener This is the listener.
*
*/
void subscribe(final Listener<T> listener);
/**
* Detach a listener.
*/
void unsubscribe();
/**
* finished notifying.
*/
void onCompleted();
/**
* regular event processing.
*/
void onNext(T notification);
/**
* failed event processing.
*/
void onError(Throwable exception);
}
package com.sms.event;
/**
* Listener interface for receiving notifications.
* @param <T> Notification type.
*/
@FunctionalInterface
public interface Listener<T> {
/**
* Attach a notifier for notification type T.
* @param notifier This is the notifier.
*
*/
void subscribe(final Notifier<T> notifier);
/**
* Detach a notifier.
*/
void unsubscribe();
/**
* finished notifying.
*/
void onCompleted();
/**
* regular event processing.
*/
void onNext(T notification);
/**
* failed event processing.
*/
void onError(Throwable exception);
}
package com.sms.event;
import java.util.concurrent.Executors;
import java.util.concurrent.ExecutorService;
import java.util.Map;
import java.util.HashMap;
import javax.annotation.concurrent.GuardedBy;
import lombok.Data;
import lombok.Synchronized;
import lombok.extern.slf4j.Slf4j;
/**
* Equivalent of a message broker.
* @param <T> Type of notification.
*/
@Slf4j
public class NotificationSystem<T extends Notification> {
@GuardedBy("$lock")
private final Map<String, Notifier<T>> notifierMap = new HashMap<String, Notifier<T>>();
private final Map<String, Listener<T>> listenerMap = new HashMap<String, Listener<T>>();
private final ExecutorService executorService = Executors.newFixedThreadPool(1);
@SuppressWarnings({ "unchecked", "rawtypes" })
@Synchronized
public void addListener(final String type,
final Listener<T> listener) {
if (!isListenerPresent(listener)) {
listenerMap.put(type, listener);
}
}
/**
* This method will notify listeners.
*
* @param notification Notification.
* @param <T> Type of notification.
*/
@Synchronized
public void notify(final T notification) {
String type = notification.getClass().getSimpleName();
Listener<T> listener = listenerMap.get(type);
log.info("Executing listener of type: {} for notification: {}", type, notification);
executorService.submit(() -> {
try {
listener.onNext(notification);
} catch (Throwable ex) {
listener.onError(ex);
}
});
}
@Synchronized
public void removeListener(final String type, final Listener<T> listener) {
listenerMap.remove(type);
}
private boolean isListenerPresent(final Listener<T> listener) {
return listenerMap.values().stream().anyMatch(le -> le.equals(listener));
}
@SuppressWarnings({ "unchecked", "rawtypes" })
@Synchronized
public void addNotifier(final String type,
final Notifier<T> notifier) {
if (!isNotifierPresent(notifier)) {
notifierMap.put(type, notifier);
}
}
@Synchronized
public void removeNotifier(final String type, final Notifier<T> notifier) {
notifierMap.remove(type);
}
public boolean isNotifierPresent(final Notifier<T> notifier) {
return notifierMap.values().stream().anyMatch(n -> n.equals(notifier));
}
public boolean isSubscriberPresent(final Listener<T> listener) {
return listenerMap.values().stream().anyMatch(l -> l.equals(listener));
}
}
No comments:
Post a Comment